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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <link rel="icon" type="image/ico" href="https://it-in-industry.com/index_files/icon.gif"/> <title>NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets | Huy Quang Pham, Duc Tran, Ninh Bao Duong, Philippe Fournier-Viger and Alioune Ngom | INFORMATION TECHNOLOGY IN INDUSTRY</title> <meta name="description" content="NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets"> <meta name="keywords" content="Association rule, minimal association rule, kernel and extendable set, frequent itemset, closed frequent itemset, mining frequent itemset from closed frequent itemset, NUCLEAR."> <meta name="DC.Contributor.Sponsor" xml:lang="en" content=""> <meta name="DC.Creator.PersonalName" content="Huy Quang Pham"> <meta name="DC.Creator.PersonalName" content="Duc Tran"> <meta name="DC.Creator.PersonalName" content="Ninh Bao Duong"> <meta name="DC.Creator.PersonalName" content="Philippe Fournier-Viger"> <meta name="DC.Creator.PersonalName" content="Alioune Ngom"> <meta name="DC.Date.created" scheme="ISO8601" content="2019-08-27"> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2019-08-27"> <meta name="DC.Date.issued" scheme="ISO8601" content="2019-08-27"> <meta name="DC.Date.modified" scheme="ISO8601" content="2019-08-27"> <meta name="DC.Description" xml:lang="en" content="Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of the generators. It can enumerate generators as well by similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs."> <meta name="DC.Format" scheme="IMT" content="application/pdf"> <meta name="DC.Identifier" content="94"> <meta name="DC.Identifier.pageNumber" content="1-13"> <meta name="DC.Identifier.URI" content="https://it-in-industry.com/issue/archive/94.html"> <meta name="DC.Language" scheme="ISO639-1" content="en"> <meta name="DC.Rights" content="Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of the generators. It can enumerate generators as well by similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs."> <meta name="DC.Source" content="INFORMATION TECHNOLOGY IN INDUSTRY"> <meta name="DC.Source.ISSN" content="2203-1731"> <meta name="DC.Source.Issue" content="2"> <meta name="DC.Source.URI" content="http://www.it-in-industry.com"> <meta name="DC.Source.Volume" content="7"> <meta name="DC.Subject" xml:lang="en" content="Association rule, minimal association rule"> <meta name="DC.Subject" xml:lang="en" content="kernel and extendable set, frequent itemset"> <meta name="DC.Subject" xml:lang="en" content="closed frequent itemset"> <meta name="DC.Subject" xml:lang="en" content="mining frequent itemset from closed frequent itemset, NUCLEAR"> <meta name="DC.Title" content="NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets"> <meta name="DC.Type" content="Text.Serial.Journal"> <meta name="DC.Type.articleType" content="Peer-Reviewed Articles"> <meta name="gs_meta_revision" content="1.1"> <meta name="citation_journal_title" content="INFORMATION TECHNOLOGY IN INDUSTRY"> <meta name="citation_issn" content="2203-1731"> <meta name="citation_author" content="Huy Quang Pham"> <meta name="citation_author_institution" content="University of Windsor, Windsor, Ontario, Canada"> <meta name="citation_title" content="NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets"> <meta name="citation_date" content="2019/08/27"> <meta name="citation_volume" content="7"> <meta name="citation_issue" content="2"> <meta name="citation_firstpage" content="1-13"> <meta name="citation_abstract_html_url" content="https://it-in-industry.com/issue/archive/94.html"> <meta name="citation_language" content="en"> <meta name="citation_keywords" xml:lang="en" content="Association rule"> <meta name="citation_keywords" xml:lang="en" content="minimal association rule, kernel and extendable set"> <meta name="citation_keywords" xml:lang="en" content="frequent itemset, closed frequent itemset"> <meta name="citation_keywords" xml:lang="en" content="mining frequent itemset from closed frequent itemset, NUCLEAR."> <meta name="citation_pdf_url" content="94.html"> <link rel="stylesheet" href="index_files/pkp.css" type="text/css"> <link rel="stylesheet" href="index_files/common.css" type="text/css"> <link rel="stylesheet" href="index_files/articleView.css" type="text/css"> <link rel="stylesheet" href="index_files/sidebar.css" type="text/css"> <link rel="stylesheet" href="index_files/leftSidebar.css" type="text/css"> <link rel="stylesheet" href="index_files/rightSidebar.css" type="text/css"> <link rel="stylesheet" href="index_files/bothSidebars.css" type="text/css"> <link rel="stylesheet" href="index_files/journalStyleSheet.css" type="text/css">  <script type="text/javascript" src="index_files/jsapi"></script> 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